18 results about "Semantic computing" patented technology

The invention provides a behavior verification method and device of an information physical system, electronic equipment and a medium, and the method comprises the steps: for a to-be-verified function of the to-be-verified information physical system, obtaining a mixed operation signal which is generated in the operation process of the system and is related to the to-be-verified function; obtaining a time-sequence point to be verified; calling a pre-defined mixed signal sequential logic formula for formally describing the function to be verified; utilizing Boolean semantics to judge whether the mixed operation signal meets a mixed signal sequential logic formula on a time-sequence point or not; and / or, calculating a robustness value of the mixed signal sequential logic formula on a time-sequence point by using robust semantics so as to quantify the degree of meeting or violating the mixed signal sequential logic formula; and determining whether the to-be-verified function passes verification or not according to the judgment result of the Boolean semantics or the robustness value. Unified processing and verification of the mixed operation signals are realized through the method.

The invention discloses a behavior insight and perception enhancement method and system based on digital twinning, and relates to the technical field of computer data processing and artificial intelligence. The method comprises the following steps: firstly collecting a computer terminal operation behavior log of a user as basic data mapping; performing semantic understanding and feature extraction on the discrete behavior logs by using a large language model as a semantic calculation engine and combining a preset cue word engineering technology, and constructing a digital behavior twin model capable of mapping behavior habits, intentions and psychological states of the user; based on the twinning model, generating a behavior insight report including multiple dimensions such as personal portraits, staged review, target tracking prediction and the like; and finally, by integrating an interactive interface of the visual board and the natural language dialogue ability, the user can visually view and interact with the digitized behavior of the user. According to the method, low-value original behavior data can be converted into high-value semantic intelligence, and the perception depth and the self-optimization capability of a user on a digital behavior mode of the user are remarkably enhanced.

The application belongs to the field of semantic computing and knowledge base construction, and particularly relates to a semantic knowledge base construction system, method and device, aiming to solve the problem that the existing semantic knowledge base construction system based on a data lake cannot describe rules with more semantic connotations, and the existing rule engine does not allow users to set independently, resulting in that the constructed semantic knowledge base cannot be applied across platforms. The system comprises a data acquisition module, a first rule generation module, a second rule generation module, a cost calculation module, a rule optimization module, a rule subnetwork generation and pre-allocation module, a rule subnetwork division module, a semantic feature data extraction module and a semantic knowledge base construction module. The application extracts rules with more connotations, models the semantic rules of users through a structured rule description language, and further realizes the cross-platform application of the semantic knowledge base.

The application discloses a data query method and device, electronic equipment and readable storage medium. The method comprises the following steps: after receiving at least one semantic query statement, mapping information of a metadata graph atlas ontology is acquired, wherein the mapping information comprises a mapping relationship between a graph attribute class in the metadata graph atlas ontology for describing a data structure of a target data source and configuration file information of a storage file in the target data source; according to the mapping relationship and a graph attribute class corresponding to the semantic query statement, configuration file information corresponding to the semantic query statement is queried; a target storage file corresponding to the configuration file information is extracted from the target data source; the target storage file is converted into a target data set based on the metadata graph atlas ontology; and semantic calculation is performed on the target data set to obtain a query result corresponding to the semantic query statement. The application solves the technical problem that the number of data islands increases rapidly in the prior art, which leads to excessive management workload of data and reduces data management efficiency.

This application discloses a method, system, apparatus, and medium for processing document semantic information. The method involves acquiring an information document to be parsed, parsing the document to obtain all measurable quantity information units within it, mapping each measurable quantity information unit to an RGB channel to obtain a mapped color for each unit, constructing a mapped color image based on the mapped colors of all the measurable quantity information units, and performing corresponding processing tasks based on the mapped color image. This method combines color calculation with semantic calculation of measurable quantity information, avoiding a certain degree of loss of overall semantic information and effectively representing the overall semantic information of measurable quantity information, thus aiding in semantic information processing. This application can be widely applied in the field of document information technology.

The application discloses a text semantic calculation and cognitive input evaluation method based on conceptual representation. Starting from the perspective of psycholinguistics and based on the language features of educational concepts, the application formally describes two important dimensions of the familiarity and abstractness of educational concepts, quantitatively calculates the attribute values of the familiarity and abstractness of educational concepts, and constructs a two-dimensional attribute model to better understand and apply educational concepts. The constructed "familiarity-abstractness (F-A)" model for educational concepts effectively maps educational concepts into a two-dimensional space. By quantitatively calculating the psychological and linguistic attribute values of educational concepts, online discussion text conceptualization is further realized. With the help of the model, the text semantic complexity of online discussion text can be accurately calculated, so that the cognitive input state and cognitive processing process of learners can be more deeply understood, and the cognitive input level of online discussion text can be further automatically evaluated.

The invention provides a computing power server operation and maintenance auditing method and system based on a block chain and the Internet of Things. The method comprises the steps that physical and logic operation and maintenance behaviors are collected in a unified mode, a structured event chain is constructed, behavior weights are calculated in combination with time, identity and semantics, server state difference analysis is conducted with events as anchor points, and attribution credibility is quantified. And finally, a structured auditing record containing identity, time, state abstract and credibility score is generated, and the structured auditing record is sequentially chained and stored after digital signature, so that a responsibility evidence chain which cannot be tampered and can be verified is formed, and the problems that behaviors and results are disjointed and the evidence chain is incomplete in operation and maintenance auditing are effectively solved. According to the method, cross-layer association of physical and logic behaviors, accurate attribution of operation and maintenance results and long-term credible evidence storage of auditing evidences are realized, and the method is suitable for computing power infrastructure scenes with high safety and compliance requirements.

The invention relates to the technical field of edge computing, in particular to a broadcast content dynamic scheduling method and system based on edge computing driving, and the method comprises the steps: sensing the crowd density of each region in real time through deploying a lightweight crowd detection model at an edge end, and constructing a structured region state model as a space basis for judging whether to schedule or not; a structured event intention expression mechanism of a multi-source broadcast task is introduced, the degree of adaptation between the task and the space is calculated in combination with crowd state and task type semantics, and whether a scheduling task is generated or not is screened; a multi-factor task scoring mechanism is constructed, and regional scheduling saturation and resource redundancy suppression items, priority ranking of broadcast tasks and space competition management are introduced; an execution task package is constructed in real time according to a scheduling plan and an equipment state, a judgment function for judging whether a task can be executed and a mode control mechanism are fused, execution actions such as code rate reduction playing, playing delay and task caching are achieved, and it is ensured that broadcast scheduling is reasonable and has high execution force and high robustness.

This invention discloses an automatic construction and dynamic evolution method for domain ontology based on proactive metadata and semantic bridging, belonging to the interdisciplinary fields of knowledge graph construction, semantic computation, and data governance. The method first collects proactive metadata from multiple heterogeneous data sources, extracts semantic features, and vectorizes them. Then, it constructs a cross-data source semantic bridging graph. Based on the bridging graph, it automatically generates domain concept classes, attributes, and hierarchical structures, and optimizes confidence through a manual verification mechanism. This method further supports incremental updates after the addition of new data sources, realizing the dynamic evolution of the domain ontology. Compared with traditional manual construction methods, this invention reduces reliance on manual intervention, improves construction efficiency and semantic consistency, and is suitable for large-scale data governance and knowledge graph construction scenarios.

The invention relates to the field of information retrieval, in particular to an information consultation intelligent retrieval system and method.The system comprises a cross-table index module, a retrieval feature module, a statement processing module, a granularity retrieval module and a resource allocation module, the cross-table index module is used for constructing an index structure, and the retrieval feature module is used for synthesizing feature vectors; the query module is used for inputting query data, outputting a retrieval basis vector, the statement processing module is used for receiving a consultation text and a projection granularity text, the granularity retrieval module is used for retrieving resources and outputting a retrieval result, and the resource allocation module is used for constructing a vector tree and allocating retrieval resources. According to the method, the retrieval generalization ability is enhanced, the comprehensiveness of the retrieval content is improved, the time complexity is low, maintenance and expansion are convenient, the expenditure of retrieval semantic calculation is reduced, the retrieval efficiency is improved, and the more stable retrieval performance is achieved.

This invention discloses a lightweight method and system for BIM models, specifically relating to the fields of computer-aided design and building information modeling (BIM). The method includes parsing the original BIM model to extract geometric, semantic, and parametric information of components; calculating the comprehensive importance of components based on engineering semantics; dividing non-critical areas into high and low priority categories through clustering; and employing an edge-folding algorithm for differentiated geometric simplification. Simultaneously, it identifies and merges identical components, establishing a geometric template library for reuse; furthermore, it selects core attributes based on the engineering scenario and performs layered compression of geometric, attribute, and index data; finally, it integrates and encapsulates the data into a structured lightweight data package, which is then output after quality assessment and iterative optimization. This invention improves compression ratio while ensuring critical accuracy, intelligently eliminates redundancy, and ensures that the lightweight model possesses high quality and strong engineering applicability.

The invention provides a semantic style-based generative text watermark embedding method and a detection method thereof, and the method comprises two stages: watermark embedding and watermark detection: during embedding, dynamically generating a style fingerprint according to preamble semantics and mapping the style fingerprint into a style instruction; selecting a sentence with the complexity closest to the target style from a plurality of candidate sentences according to the style instruction to embed the watermark in a hidden manner, and generating a subsequent text in a chain recursion manner; during detection, style complexity observation values of sentences in a to-be-detected text are extracted and binarized, binarized style fingerprints are obtained, segmentation is carried out according to the same length, the matching degree of theoretical fingerprints obtained through semantic calculation in the previous text and binary style fingerprints extracted in the current segment is compared segment by segment, and the existence of watermarks is judged based on statistical hypothesis testing. And blind detection without an original model or cue word is realized. According to the method, the watermark signal is embedded into the text style dimension, so that the high quality of the text is ensured, and meanwhile, the method has high concealment and strong robustness.

This invention provides a dynamic perception method for AI-based computing power traffic control for edge computing, relating to the field of data exchange network management technology. The invention constructs a semantic computing power topology ternary graph model, combining the physical information network computational link affinity coefficient and computational symmetry to screen candidate shadow path sets. Under a hierarchical reinforcement learning framework, it uses fusion features such as state synchronization entropy and consistency deviation to determine shadow preheating nodes and initiate dual traffic transmission. A composite reward signal is constructed based on a risk-adjusted continuity index and multi-dimensional penalty terms to guide the policy network convergence. Seamless task takeover and migration are achieved through microsecond-level redirection via an SDN gateway. Finally, the robustness coefficient of heterogeneous computing power is calculated by injecting background interference load, dynamically updating the physical layer screening admission threshold. This invention overcomes the defect of easy disconnection during task migration in highly dynamic edge scenarios, achieving low-latency smooth flow of computing power, system risk resistance, and adaptive fault tolerance.

The invention relates to the technical field of semantic network reasoning, in particular to a knowledge base and map collaborative enhancement large model reasoning system and method, and the system comprises a structure guiding module, a semantic difference module, a stability extraction module, a path comparison module and a label binding module. According to the method, graph path node semantic vectors and channel numbers are extracted, response entity fitting degrees and sequences are combined, structured distribution information is constructed, semantic path recognition precision is enhanced, difference entities are screened on the basis of semantic distances, and stability parameters are generated in combination with context word frequencies, co-occurrence frequencies and document distribution; semantic conflicts are positioned, paragraph stability is quantified, alignment of semantic contents and structural paths is achieved, causal chain consistency is improved, the relation between response paragraphs and path nodes is marked, traceable chains are formed, reasoning transparency and result verifiability are enhanced, and semantic calculation, structure extraction, sequence judgment and other means are fused. And the reasoning accuracy, interpretability and credibility are improved.

The application relates to a business semantic recognition method, which comprises the following steps: step S1, receiving an English character string and other context information of a to-be-recognized semantic; step S2, pre-processing the to-be-recognized field; step S3, segmenting the to-be-recognized field to obtain a plurality of segmentation schemes; step S4, for each segmentation scheme, using a transition probability matrix to calculate a probability score at a word segmentation point; step S5, for each word in the segmentation scheme, judging whether the word is a non-pinyin through a pinyin semantic DFA tree, and then performing one-by-one matching with a word entry in a word library and calculating a similarity score; step S6, taking a semantic of a word entry with the highest similarity score to calculate a correlation score between semantics of the whole matching scheme; step S7, extracting a matching scheme with the highest comprehensive score and splicing the matching scheme as a recommended label; and step S8, outputting a recommended Chinese business semantic and a recommendation index. The application improves the efficiency of metadata carding work.

The application provides a semantic style-based generative text watermark embedding method and a detection method thereof, which comprises two stages of watermark embedding and watermark detection: in the embedding stage, a style fingerprint is dynamically generated according to the previous text semantics and is mapped into a style instruction, a sentence closest to the target style complexity is selected from multiple candidate sentences according to the style instruction to implicitly embed the watermark, and subsequent text is generated by chain recursion; in the detection stage, the style complexity observation value of each sentence in the text to be detected is extracted and binarized to obtain a binary style fingerprint, the binary style fingerprint is segmented according to the same length, the matching degree of the theoretical fingerprint calculated according to the previous text semantics and the binary style fingerprint extracted from the current segment is compared, the watermark existence is judged based on statistical hypothesis testing, and blind detection without the original model or prompt words is realized. The watermark signal is embedded in the text style dimension, high quality of the text is ensured, and high concealment and strong robustness are combined.

The application relates to the field of information retrieval, in particular to an information consultation intelligent retrieval system and method based on cloud services, which comprises a cross-table index module, a retrieval characteristic module, a sentence processing module, a granularity retrieval module and a resource allocation module; the cross-table index module is used for constructing an index structure; the retrieval characteristic module is used for synthesizing a characteristic vector and outputting a retrieval basis vector; the sentence processing module is used for receiving a consultation text and projecting a granularity text; the granularity retrieval module is used for retrieving resources and outputting a retrieval result; and the resource allocation module is used for constructing a vector tree and allocating retrieval resources; the application can improve retrieval precision in a domain, relieve data expansion pressure of a large-scale database, enhance retrieval generalization capability, improve comprehensiveness of retrieval content, reduce time complexity, facilitate maintenance and expansion, reduce the cost of retrieval semantic calculation, improve retrieval efficiency, and realize more stable retrieval performance.

One example method includes obtaining user interaction data generated during an interaction between a human user and a computing entity, extracting contextual information from the user interaction data, based at least in part on the contextual information, scheduling and assigning a priority to a semantic task, based at least in part on the priority, allocating resources for performance of the semantic task, generating, for the semantic task, a response to the human user, and the response is generated based in part on the contextual information, and outputting the response.